Dr.  João Portela Gama Digital Library

Based in Porto, Portugal
João Portela Gama


João Gama is Associate Professor of the Faculty of Economy, University of Porto. He is a researcher and vice-director of LIAAD, a group belonging to INESC TEC. He got the PhD degree from the University of Porto, in 2000. He has worked in several National and European projects on Incremental and Adaptive learning systems, Ubiquitous Knowledge Discovery, Learning from Massive, and Structured Data, etc.

He served as Co-Program chair of ECML'2005, DS'2009, ADMA'2009, IDA' 2011, and ECML/PKDD'2015. He served as track chair on Data Streams with ACM SAC from 2007 till 2016. He organized a series of Workshops on Knowledge Discovery from Data Streams with ECML/PKDD, and Knowledge Discovery from Sensor Data with ACM SIGKDD.

He is author of several books in Data Mining (in Portuguese) and authored a monograph on Knowledge Discovery from Data Streams. He authored more than 250 peer-reviewed papers in areas related to machine learning, data mining, and data streams.

He is a member of the editorial board of international journals ML, DMKD, TKDE, IDA, NGC, and KAIS. He supervised more than 12 PhD students and 50 Msc students.

João Gama is member of SIGKDD and SIGAPP. From 2007 till 2017 he  co-chaired the Data Streams track at ACM SIGAPP Symposium on Applied Computing. In conjunction with SIGKDD he co-chaired workshops on Knowledge Discovery from Sensor Data (2007-2008-2010). His ACM digital library author profile: http://dl.acm.org/author_page.cfm?id=81100028845&coll=DL&dl=GUIDE#


Available Lectures

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  • Data Stream Mining for Ubiquitous Environments
     Data stream mining is, nowadays, a mature topic in data mining. Nevertheless, most of the works focus on centralized approaches to learn from sequences of instances generated from...
  • Evolving Social Networks: trajectories of communities

    In recent years we witnessed an impressive advance in the social networks field, which became a ”hot” topic and a focus of considerable attention. The...

  • Real-Time Data Mining
    Nowadays, there are applications in which the data are modelled best not as persistent tables, but rather as transient data streams. In this keynote, we discuss the limitations of current...

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